AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The gcp-to-aws skill provides a deterministic, multi-phase process to migrate Google Cloud Platform (GCP) workloads to AWS. It supports infrastructure migration via Terraform analysis and AI workload migration by mapping models to Amazon Bedrock. The skill enforces a strict state machine, requiring discovery, clarification, design, estimation, and generation phases to ensure a reliable migration path. It includes specific logic for handling hybrid stacks, AI provider transitions, and complex infrastructure clustering.
What it does
- Discovers GCP resources from Terraform, application code, or billing data.
- Clarifies migration requirements through a structured questionnaire.
- Designs AWS architecture based on GCP workload types and AI model requirements.
- Estimates costs for the proposed AWS infrastructure and AI services.
- Generates migration artifacts including Terraform configurations and AI provider adapters.
- Provides specialized guidance for migrating AI workloads from Gemini or OpenAI to Amazon Bedrock.
When to use it
- When you need to migrate existing GCP infrastructure to AWS.
- When you are looking to move AI-driven applications from GCP or OpenAI to Amazon Bedrock.
- When you have Terraform files or billing exports and need a structured migration plan.
- When you require a step-by-step, validated approach to re-platforming cloud services.
Advantages
- Enforces a rigorous, fail-closed state machine to prevent skipped steps.
- Provides intelligent mapping of GCP services to AWS equivalents.
- Includes specific logic for AI workload migration and model compatibility.
- Supports multi-signal discovery using IaC, code, and billing data.
- Ensures consistent artifact generation through a structured 6-phase process.
Usage examples
- “Migrate my GKE and Cloud SQL setup to AWS.”
- The agent initiates the discovery phase, analyzing your Terraform files to identify GKE and Cloud SQL resources, then guides you through the clarification and design phases for EKS and RDS.
- “I need to move my LangChain app from OpenAI to Bedrock.”
- The agent identifies the AI workload, selects the best-fit Bedrock model family, and generates the necessary provider adapters and migration documentation.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/cline/gcp-to-aws/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gcp-to-aws.zip -d .agents/skills
Documentation by Cline
GCP-to-AWS Migration Skill
Philosophy
Re-platform by default: Select AWS services that match GCP workload types (e.g., Cloud Run → Fargate, Cloud SQL → RDS).
Dev sizing unless specified: Default to development-tier capacity (e.g., db.t4g.micro, single AZ). Upgrade only on user direction.
No human one-time migration costs: Do not present human labor, professional services, or people-time work as dollar estimates or "one-time migration cost" budget categories. Vendor charges grounded in data (for example GCP data transfer egress in the infra estimate when billing exists) are allowed.
Multi-signal approach: Design phase adapts based on available inputs — Terraform IaC for infrastructure, billing data for service mapping, and app code for AI workload detection.
BigQuery /
google_bigquery_*: The skill does not recommend a specific AWS analytics or warehouse service. During Clarify, if discovery shows BigQuery (IaCgoogle_bigquery_*and/or billing rows for BigQuery), you must surface the specialist advisory before Design (seereferences/phases/clarify/clarify.md). Design output usesDeferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (seereferences/phases/design/design-infra.mdBigQuery specialist gate).
Definitions
"Load" = Read the file using the Read tool and follow its instructions. Do not summarize or skip sections.
$MIGRATION_DIR= The run-specific directory under.migration/(e.g.,.migration/0226-1430/). Set during Phase 1 (Discover).
Context Loading Rules
Each phase loads reference files on demand. To keep per-turn context manageable and prevent instruction-following degradation:
Budget: Each phase should load no more than ~800 lines of instructions (excluding user artifacts like JSON profiles and MCP tool results).
Conditional loading: Reference files with trigger conditions (e.g.,
agentic_profile.is_agentic == true) MUST NOT be loaded unless the condition is met. Do not speculatively load files.No duplication: Model mapping tables, pricing data, and shared warnings exist in one canonical file. Other files reference them; they do not copy them inline.
Progressive depth: Phase orchestrators (
design.md,generate.md) contain short routing logic that points to detailed sub-files. Load the sub-file only when its path is selected.
Conditional reference files (load ONLY when condition is true):
| File | Condition |
|---|---|
design-refs/ai-gemini-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "gemini" or "both" |
design-refs/ai-openai-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "openai" or "both" |
design-refs/ai-anthropic-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "anthropic" |
design-refs/ai.md | ai-workload-profile.json exists AND summary.ai_source = "other" |
design-refs/design-ref-harness.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness" |
design-refs/design-ref-agentic-to-agentcore.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands" |
shared/retarget-gotchas.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "retarget" |
When adding new reference files, verify the phase's total loaded instructions remain under budget. If a new file would exceed ~800 lines when combined with other loaded refs, split it or make it conditional.
Hybrid stack budget warning:
When both gcp-resource-inventory.json AND ai-workload-profile.json exist, the combined design refs will approach the ~800-line budget. Output this warning to the user before loading the AI design refs:
"⚠️ This is a large hybrid stack (infrastructure + AI workloads). To ensure complete and accurate recommendations, consider running the migration in two separate passes:
Pass 1 — Infrastructure: Run with only your Terraform files to get infra mapping, Terraform generation, and cost estimates.
Pass 2 — AI workloads: Run with only your application code to get Bedrock model recommendations, provider adapters, and AI migration artifacts.
Continue with the combined run? (Y/N)"
If the user chooses to continue, proceed with the combined run. Load AI refs after infra refs to preserve infra instruction fidelity. If the user declines, stop and instruct them to re-run with a single input source type.
This warning is advisory only — it does not block the run.
Prerequisites
User must provide at least one GCP source:
Terraform IaC:
.tffiles (with optional.tfvars,.tfstate)Application code: Source files with GCP SDK or AI framework imports
Billing data: GCP billing/cost/usage export files (CSV or JSON)
If none of the above are found, stop and ask user to provide at least one source type.
State Machine
This is the execution controller. After completing each phase, consult this table to determine the next action.
| Current State | Condition | Next Action |
|---|---|---|
discover | phases.discover != "completed" | Load references/phases/discover/discover.md |
clarify | phases.discover == "completed" AND phases.clarify != "completed" | Load references/phases/clarify/clarify.md |
design | phases.clarify == "completed" AND phases.design != "completed" | Load references/phases/design/design.md |
estimate | phases.design == "completed" AND phases.estimate != "completed" | Load references/phases/estimate/estimate.md |
generate | phases.estimate == "completed" AND phases.generate != "completed" | Load references/phases/generate/generate.md |
complete | phases.generate == "completed" AND phases.feedback == "pending" | Set phases.feedback to "completed" (user had two chances), then migration complete |
complete | phases.generate == "completed" AND phases.feedback == "completed" | Migration planning complete |
How to determine current state (deterministic):
Read
$MIGRATION_DIR/.phase-status.jsonIf
current_phaseexists, use it (must match one of: discover, clarify, design, estimate, generate, complete)Otherwise use ordered phase evaluation:
discover→clarify→design→estimate→generatePick the first phase in that order where
phases.<phase> != "completed"; if none, state iscomplete
Phase gate checks: If prior phase incomplete, do not advance (e.g., cannot enter estimate without completed design).
Clarify is mandatory: Do not load references/phases/design/design.md, references/phases/estimate/estimate.md, or references/phases/generate/generate.md unless $MIGRATION_DIR/.phase-status.json exists and phases.clarify is exactly "completed". A preferences.json file alone is not sufficient proof that Clarify ran. If the user asks to skip Clarify or jump straight to Design, cost estimate, or artifact generation, refuse briefly, then load references/phases/clarify/clarify.md and run Phase 2. There is no exception for "quick" or "obvious" migrations.
Feedback checkpoints: Feedback is not a sequential phase — it is offered at two interleaved checkpoints (after Discover and after Estimate). See the Feedback Checkpoints section below for details.
Handoff Gate Orchestration (Fail Closed)
Load references/shared/handoff-gates.md when executing any phase completion step.
Single
$MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across.migration/*/sessions.Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from
$MIGRATION_DIR/. Do not rely on chat memory.Advance only on
HANDOFF_OK: A phase is complete only when its orchestrator emitsHANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.On
GATE_FAIL: Output the failure line(s) to the user in plain language. Do NOT modify artifacts to pass the gate. Do NOT continue to the next phase. Tell the user which phase to re-run.Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to
"pending". Seehandoff-gates.mdre-entry table.
Generate phase additionally loads references/shared/validate-artifacts.md before writing migration-report.html.
State Validation
When reading $MIGRATION_DIR/.phase-status.json, validate before proceeding:
Multiple sessions: If multiple directories exist under
.migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.Invalid JSON: If
.phase-status.jsonfails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."Unrecognized phase: If
phasesobject contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."Unrecognized status: If any
phases.*value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."Invalid
current_phase(if present): Ifcurrent_phaseis not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."Out-of-order completion: For ordered phases [discover, clarify, design, estimate, generate], if any later phase is
"completed"while an earlier phase is not"completed", STOP. Output: "Inconsistent phase ordering detected. Reconcile.phase-status.jsonbefore resuming."Multiple active phases: Across core phases {discover, clarify, design, estimate, generate}, at most one phase may be
"in_progress". If >1, STOP. Output: "Multiple phases are in_progress. Keep only one active phase before resuming."
State Management
Migration state lives in $MIGRATION_DIR (.migration/[MMDD-HHMM]/), created by Phase 1 and persisted across invocations.
.phase-status.json schema:
{
"migration_id": "0226-1430",
"last_updated": "2026-02-26T15:35:22Z",
"current_phase": "design",
"phases": {
"discover": "completed",
"clarify": "completed",
"design": "in_progress",
"estimate": "pending",
"generate": "pending",
"feedback": "pending"
}
}Status values: "pending" → "in_progress" → "completed". Never goes backward.
For core phases (discover, clarify, design, estimate, generate), at most one phase may be "in_progress" at any time.
current_phase is optional but recommended; when present it is authoritative.
The .migration/ directory is automatically protected by a .gitignore file created in Phase 1.
Phase Status Update Protocol
Use read-merge-write updates for .phase-status.json:
Read the current file before every update.
Change only the phase keys being advanced and
last_updated.Keep prior completed phases unchanged.
Set
current_phaseto the next deterministic phase (orcompleteafter generate).Write the full file in the same turn as your final phase work message.
Example — after completing the Clarify phase, write $MIGRATION_DIR/.phase-status.json with:
{
"migration_id": "MMDD-HHMM",
"last_updated": "2026-02-26T15:35:22Z",
"current_phase": "design",
"phases": {
"discover": "completed",
"clarify": "completed",
"design": "pending",
"estimate": "pending",
"generate": "pending",
"feedback": "pending"
}
}Replace MMDD-HHMM with the actual migration ID, generate the last_updated ISO 8601 UTC timestamp yourself, and set each phase to its correct status at that point.
Phase Summary Table
| Phase | Inputs | Outputs | Reference |
|---|---|---|---|
| Discover | .tf files, app source code, and/or billing exports (at least one required) | gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json, .phase-status.json updated (outputs vary by input) | references/phases/discover/discover.md |
| Clarify | Discovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist) | preferences.json, .phase-status.json updated | references/phases/clarify/clarify.md |
| Design | preferences.json + discovery artifacts | aws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only) | references/phases/design/design.md |
| Estimate | aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | estimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updated | references/phases/estimate/estimate.md |
| Generate | estimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | generation-infra.json or generation-ai.json or generation-billing.json + terraform/, scripts/, ai-migration/, validation-report.json (when infra route active), MIGRATION_GUIDE.md, README.md, .phase-status.json updated | references/phases/generate/generate.md |
| Feedback | .phase-status.json (discover completed minimum), all existing migration artifacts | feedback.json, trace.json, .phase-status.json updated | references/phases/feedback/feedback.md |
MCP Servers
awspricing (for cost estimation):
Provides
get_pricing,get_pricing_service_codes,get_pricing_service_attributestoolsOnly needed during Estimate phase. Discover and Design do not require it.
Primary pricing source:
references/shared/pricing-cache.md(cached 2026 rates, ±5-10% for infrastructure, ±15-25% for AI models). MCP is secondary — used only for services not found in the cache.
Files in This Skill
gcp-to-aws/
├── SKILL.md ← You are here (orchestrator + state machine)
│
├── references/
│ ├── phases/
│ │ ├── discover/
│ │ │ ├── discover.md # Phase 1: Discover orchestrator
│ │ │ ├── discover-iac.md # Terraform/IaC discovery
│ │ │ ├── discover-app-code.md # App code discovery
│ │ │ └── discover-billing.md # Billing data discovery
│ │ ├── clarify/
│ │ │ ├── clarify.md # Phase 2: Clarify orchestrator
│ │ │ ├── clarify-global.md # Category A: Global/Strategic (Q1-Q7)
│ │ │ ├── clarify-compute.md # Categories B+C: Config Gaps + Compute (Q8-Q11)
│ │ │ ├── clarify-database.md # Category D: Database (Q12–Q13b)
│ │ │ ├── clarify-ai.md # Category F: AI/Bedrock (Q14-Q22)
│ │ │ └── clarify-ai-only.md # Standalone AI-only migration flow
│ │ ├── design/
│ │ │ ├── design.md # Phase 3: Design orchestrator
│ │ │ ├── design-infra.md # Infrastructure design (IaC-based)
│ │ │ ├── design-ai.md # AI workload design (Bedrock)
│ │ │ └── design-billing.md # Billing-only design (fallback)
│ │ ├── estimate/
│ │ │ ├── estimate.md # Phase 4: Estimate orchestrator
│ │ │ ├── estimate-infra.md # Infrastructure cost analysis
│ │ │ ├── estimate-ai.md # AI workload cost analysis
│ │ │ └── estimate-billing.md # Billing-only cost analysis
│ │ ├── generate/
│ │ │ ├── generate.md # Phase 5: Generate orchestrator
│ │ │ ├── generate-infra.md # Infrastructure migration plan
│ │ │ ├── generate-ai.md # AI migration plan
│ │ │ ├── generate-billing.md # Billing-only migration plan
│ │ │ ├── generate-artifacts-infra.md # Terraform configurations
│ │ │ ├── generate-artifacts-scripts.md # Migration scripts
│ │ │ ├── generate-artifacts-ai.md # Provider adapter + test harness
│ │ │ ├── generate-artifacts-billing.md # Skeleton Terraform
│ │ │ └── generate-artifacts-docs.md # MIGRATION_GUIDE.md + README.md
│ │ └── feedback/
│ │ ├── feedback.md # Phase 6: Feedback orchestrator
│ │ └── feedback-trace.md # Anonymized trace builder
│ │
│ ├── design-refs/
│ │ ├── index.md # Lookup table: GCP type → design-ref file
│ │ ├── fast-path.md # Deterministic 1:1 mappings (Pass 1)
│ │ ├── compute.md # Compute mappings (Cloud Run, GCE, GKE, etc.)
│ │ ├── database.md # Database mappings (Cloud SQL, Spanner, etc.)
│ │ ├── storage.md # Storage mappings (GCS, Filestore, etc.)
│ │ ├── networking.md # Networking mappings (VPC, LB, DNS, etc.)
│ │ ├── messaging.md # Messaging mappings (Pub/Sub, etc.)
│ │ └── ai.md # AI mappings (Vertex AI → Bedrock)
│ │
│ ├── clustering/terraform/
│ │ ├── classification-rules.md # Primary/secondary classification
│ │ ├── clustering-algorithm.md # Cluster formation rules
│ │ ├── depth-calculation.md # Topological depth calculation
│ │ └── typed-edges-strategy.md # Edge type assignment
│ │
│ └── shared/
│ ├── schema-phase-status.md # .phase-status.json schema (canonical reference)
│ ├── schema-discover-iac.md # gcp-resource-inventory + clusters schemas (loaded by discover-iac.md)
│ ├── schema-discover-ai.md # ai-workload-profile schema (loaded by discover-app-code.md and discover-iac.md Step 7d)
│ ├── schema-discover-billing.md # billing-profile schema (loaded by discover-billing.md)
│ ├── schema-estimate-infra.md # estimation-infra.json schema (loaded by estimate-infra.md at write time)
│ ├── handoff-gates.md # Fail-closed phase handoff protocol (GATE_FAIL / HANDOFF_OK)
│ ├── validate-artifacts.md # Pre-report validation (Generate Step 0; read-only)
│ ├── migration-complexity.md # Complexity tier definitions (small/medium/large) for timeline scaling
│ ├── pricing-cache.md # Cached AWS + source provider pricing (±5-25%, primary source)
│ └── bedrock-quotas.md # Bedrock TPM/RPM quota awareness, burndown rates, capacity planning| Condition | Action |
|---|---|
No GCP sources found (no .tf, no app code, no billing data) | Stop. Output: "No GCP sources detected. Provide at least one source type (Terraform files, application code, or billing exports) and try again." |
.phase-status.json missing phase gate | Stop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1." |
| awspricing unavailable after 3 attempts | Display user warning about ±5-25% accuracy. Use pricing-cache.md. Add pricing_source: "cached_fallback" to the applicable estimation-*.json file. |
| User skips questions or says "use defaults for the rest" | Apply documented defaults for remaining questions in the current batch and all subsequent batches. Phase 2 completes either way. |
aws-design.json missing required clusters | Stop Phase 4. Output: "Re-run Phase 3 to generate missing cluster designs." |
Defaults
IaC output: Terraform configurations, migration scripts, AI migration code, and documentation
Region:
us-east-1(unless user specifies, or GCP region → AWS region mapping suggests otherwise)Sizing: Development tier (e.g.,
db.t4g.microfor databases, 0.5 CPU for Fargate)Migration mode: Adapts based on available inputs (infrastructure, AI, or billing-only)
Cost currency: USD
Timeline assumption: 2-16 weeks depending on migration complexity — small (2-6 weeks), medium (6-12 weeks), large (12-18 weeks). See
references/shared/migration-complexity.mdfor tier definitions.
Workflow Execution
When invoked, the agent MUST follow this exact sequence:
Load phase status: Read
.phase-status.jsonfrom.migration/*/.If missing: Initialize for Phase 1 (Discover)
If exists: Determine current phase using deterministic rules in State Machine
Determine phase to execute:
If
current_phaseexists: execute that phase.Otherwise execute the first non-completed phase in ordered list: discover → clarify → design → estimate → generate.
If all ordered phases are completed: migration is complete (with feedback finalization rule).
Read phase reference: Load the full reference file for the target phase.
Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.
Validate outputs: Confirm all required output files exist with correct schema before proceeding. Phase orchestrators run Completion Handoff Gate checks per
shared/handoff-gates.md.Handoff gate: Emit
HANDOFF_OKorGATE_FAILpershared/handoff-gates.md. OnGATE_FAIL, stop — do not update phase status or load the next phase.Update phase status: Only after
HANDOFF_OK. Use the Phase Status Update Protocol (read-merge-write) in the same turn as the phase's final output message.Feedback checkpoint: After a phase completes, check if feedback is due (see rules below). This runs before advancing to the next phase.
After Discover (if
phases.feedbackis"pending"): Output to user: "Would you like to share quick feedback (5 optional questions + anonymized usage data) to help improve this tool? Your data never includes resource names, file paths, or account IDs. [A] Send feedback now [B] Wait until after the Estimate phase"If user picks A → Load
references/phases/feedback/feedback.md, execute it, then continue to Clarify.If user picks B → Continue to Clarify (feedback stays
"pending").
After Estimate (if
phases.feedbackis"pending"): Output to user: "Would you like to share quick feedback now? (5 optional questions + anonymized usage data) [A] Yes, share feedback [B] No thanks, continue to Generate"If user picks A → Load
references/phases/feedback/feedback.md, execute it, then continue to Generate.If user picks B → Use the Phase Status Update Protocol to set
phases.feedbackto"completed". Continue to Generate.
After Generate: No feedback offer. If
phases.feedbackis still"pending", use the Phase Status Update Protocol to set it to"completed"(user had two chances and chose to defer/skip).
Display summary: Show user what was accomplished, highlight next phase, or confirm migration completion.
Critical constraint: Agent must strictly adhere to the reference file's workflow. If unable to complete a step, stop and report the exact step that failed.
User can invoke the skill again to resume from current_phase (or deterministic ordered evaluation when current_phase is absent).
Scope Notes
v1.0 includes:
Terraform infrastructure discovery
App code scanning (AI workload detection)
Billing data import from GCP
User requirement clarification (adaptive questions by category)
Multi-path Design (infrastructure, AI workloads, billing-only fallback)
AWS cost estimation (from pricing API or fallback)
Migration artifact generation (Terraform, scripts, AI adapters, documentation)
Optional feedback collection with anonymized telemetry
Files
- ATTRIBUTION.mdAdded by AutoGineer826 B
- LICENSELicence file added by AutoGineer10.5 KiB
- SKILL.md29.6 KiB
- data/sdk-capability-map.json1.1 KiB
- references/clustering/terraform/classification-rules.md6.5 KiB
- references/clustering/terraform/clustering-algorithm.md7.9 KiB
- references/clustering/terraform/depth-calculation.md4.5 KiB
- references/clustering/terraform/typed-edges-strategy.md4.6 KiB
- references/design-refs/ai-anthropic-to-bedrock.md2.9 KiB
- references/design-refs/ai-gemini-to-bedrock.md15.4 KiB
- references/design-refs/ai-openai-to-bedrock.md17.5 KiB
- references/design-refs/ai.md3.4 KiB
- references/design-refs/compute.md6.8 KiB
- references/design-refs/database.md9.1 KiB
- references/design-refs/design-ref-agentic-to-agentcore.md18.8 KiB
- references/design-refs/design-ref-harness.md7.9 KiB
- references/design-refs/fast-path.md10.3 KiB
- references/design-refs/index.md6.2 KiB
- references/design-refs/messaging.md3.5 KiB
- references/design-refs/networking.md5.5 KiB
- references/design-refs/security.md3.8 KiB
- references/design-refs/storage.md4.7 KiB
- references/phases/clarify/clarify-ai-only.md16.4 KiB
- references/phases/clarify/clarify-ai.md57.7 KiB
- references/phases/clarify/clarify-compute.md10.0 KiB
- references/phases/clarify/clarify-database.md10.8 KiB
- references/phases/clarify/clarify-global.md23.7 KiB
- references/phases/clarify/clarify.md50.5 KiB
- references/phases/design/design-ai.md29.6 KiB
- references/phases/design/design-billing.md12.7 KiB
- references/phases/design/design-infra.md12.8 KiB
- references/phases/design/design.md4.5 KiB
- references/phases/discover/discover-app-code.md41.7 KiB
- references/phases/discover/discover-billing.md7.2 KiB
- references/phases/discover/discover-iac.md33.0 KiB
- references/phases/discover/discover-preview.md19.9 KiB
- references/phases/discover/discover.md11.5 KiB
- references/phases/estimate/estimate-ai.md17.2 KiB
- references/phases/estimate/estimate-billing.md12.4 KiB
- references/phases/estimate/estimate-infra.md46.6 KiB
- references/phases/estimate/estimate.md8.1 KiB
- references/phases/feedback/feedback-trace.md4.5 KiB
- references/phases/feedback/feedback.md3.2 KiB
- references/phases/generate/generate-ai.md13.5 KiB
- references/phases/generate/generate-artifacts-ai.md33.6 KiB
- references/phases/generate/generate-artifacts-billing.md6.1 KiB
- references/phases/generate/generate-artifacts-docs.md21.0 KiB
- references/phases/generate/generate-artifacts-infra.md43.2 KiB
- references/phases/generate/generate-artifacts-report.md40.1 KiB
- references/phases/generate/generate-artifacts-scripts.md14.5 KiB
- references/phases/generate/generate-billing.md16.5 KiB
- references/phases/generate/generate-infra.md22.0 KiB
- references/phases/generate/generate.md7.9 KiB
- references/shared/ai-migration-guardrails.md6.6 KiB
- references/shared/ai-model-lifecycle.md8.8 KiB
- references/shared/bedrock-quotas.md7.1 KiB
- references/shared/handoff-gates.md4.7 KiB
- references/shared/migration-complexity.md11.0 KiB
- references/shared/pricing-cache.md41.2 KiB
- references/shared/pricing-fallback.md1.5 KiB
- references/shared/retarget-gotchas.md10.9 KiB
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references/design-refs/ai-openai-to-bedrock.md
OpenAI to Bedrock — Model Selection Guide
Applies to: OpenAI SDK usage detected in GCP-hosted applications → Amazon Bedrock
This file is loaded by design-ai.md when ai-workload-profile.json has summary.ai_source = "openai" or "both". It provides model mapping tables with pricing and honest competitive analysis for OpenAI → Bedrock migration decisions.
Many GCP-hosted applications use OpenAI's API rather than Vertex AI. This guide covers that migration path.
Verify all pricing via AWS Pricing MCP or references/shared/pricing-cache.md. Uses OpenAI Standard tier pricing.
Model lifecycle: Before recommending any Bedrock model, check references/shared/ai-model-lifecycle.md. Do not recommend Legacy models as primary selections for new migrations. Legacy models are annotated below where they appear.
Key Insight: The Landscape Has Changed (April 2026)
It is no longer "Bedrock is always cheaper." It depends on the model.
Bedrock cheaper: GPT-5.5 flagship (17% cheaper output via Opus 4.6), Nova Lite vs Mini models (85-94%), Nova Micro vs Nano (65-87%), Nova 2 Pro vs Pro models (90-95%), DeepSeek-R1 vs o3 (32%)
OpenAI cheaper: GPT-5.4 (5%), GPT-5.2 (50%), GPT-5.1/5 (40%), GPT-4.1 (43%), GPT-4o (29%), o4-mini/o3-mini/o1-mini (69%)
GPT-5.5 note (April 23, 2026): GPT-5.5 doubled pricing to $5/$30 per MTok vs GPT-5.4's $2.50/$15. Claude Opus 4.6 at $5/$25 now matches on input and is 17% cheaper on output. This reverses the GPT-5.4 dynamic where OpenAI was cheaper — at the GPT-5.5 tier, Bedrock wins on cost. GPT-5.5 uses 40% fewer output tokens on coding tasks (per OpenAI), partially offsetting the price hike for Codex-style workloads.
Model Mapping Tables
GPT-5.5 Series (Latest — April 23, 2026)
GPT-5.5 is the first fully retrained base model since GPT-4.5. Natively omnimodal (text + image + audio + video), 88.7% SWE-Bench Verified, 256K context in ChatGPT / 1M in API. Two variants: standard and Pro. No Mini/Nano variants at launch (expected Q3 2026). Percentages below are blended savings using a 2:1 input-to-output token ratio.
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-5.5 | $5.00 / $30.00 | Claude Opus 4.6 | $5.00 / $25.00 | Bedrock 17% cheaper |
| GPT-5.5 | $5.00 / $30.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | Bedrock 53% cheaper |
| GPT-5.5 Pro | $30.00 / $180.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 95% cheaper |
Token efficiency caveat: OpenAI reports GPT-5.5 uses ~40% fewer output tokens on Codex-style tasks vs GPT-5.4. Effective cost increase over GPT-5.4 is ~50% (not 100%) for coding workloads. For non-coding workloads, the full 2× price applies.
GPT-5.4 Series
Percentages below are blended savings using a 2:1 input-to-output token ratio. GPT-5.4 uses breakpoint pricing at 272K input tokens; rates below assume <272K context.
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-5.4 | $2.50 / $15.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | OpenAI 5% cheaper |
| GPT-5.4 Mini | $0.75 / $4.50 | Nova Lite | $0.06 / $0.24 | Bedrock 94% cheaper |
| GPT-5.4 Nano | $0.20 / $1.25 | Nova Micro | $0.035 / $0.14 | Bedrock 87% cheaper |
| GPT-5.4 Pro | $30.00 / $180.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 94% cheaper |
Flagship (GPT-5/5.2 Series)
Percentages below are blended savings using a 2:1 input-to-output token ratio.
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-5.2 | $1.75 / $14.00 | Claude Opus 4.7 / 4.6 | $5.00 / $25.00 | OpenAI 50% cheaper |
| GPT-5.1 / GPT-5 | $1.25 / $10.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | OpenAI 40% cheaper |
| GPT-5 Mini | $0.25 / $2.00 | Nova Lite | $0.06 / $0.24 | Bedrock 86% cheaper |
| GPT-5 Nano | $0.05 / $0.40 | Nova Micro | $0.035 / $0.14 | Bedrock 58% cheaper |
Pro Models (Extended Reasoning)
Lifecycle note: Nova Premier v1 is Legacy (EOL Sep 14, 2026). Nova 2 Pro (Preview) is the Active successor for reasoning-heavy workloads. Pricing differs — see
pricing-cache.md.
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-5.5 Pro | $30.00 / $180.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 95% cheaper |
| GPT-5.4 Pro | $30.00 / $180.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 94% cheaper |
| GPT-5.2 Pro | $21.00 / $168.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 93% cheaper |
| GPT-5 Pro | $15.00 / $120.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 90% cheaper |
GPT-4.1 Series
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-4.1 | $2.00 / $8.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | OpenAI 43% cheaper |
| GPT-4.1 Mini | $0.40 / $1.60 | Nova Lite | $0.06 / $0.24 | Bedrock 85% cheaper |
| GPT-4.1 Nano | $0.10 / $0.40 | Nova Micro | $0.035 / $0.14 | Bedrock 65% cheaper |
GPT-4o Series
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-4o | $2.50 / $10.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | OpenAI 29% cheaper |
| GPT-4o Mini | $0.15 / $0.60 | Nova Lite | $0.06 / $0.24 | Bedrock 60% cheaper |
Reasoning Models (o-series)
Lifecycle note: Nova Premier v1 is Legacy (EOL Sep 14, 2026). Table below uses Nova 2 Pro (Preview) as the Active replacement.
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| o1-pro | $150.00 / $600.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 98% cheaper |
| o3-pro | $20.00 / $80.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 87% cheaper |
| o1 | $15.00 / $60.00 | Nova 2 Pro (Preview) | $1.38 / $11.00 | Bedrock 83% cheaper |
| o3 | $2.00 / $8.00 | DeepSeek-R1 | $1.35 / $5.40 | Bedrock 32% cheaper |
| o4-mini / o3-mini / o1-mini | $1.10 / $4.40 | Claude Sonnet 4.6 | $3.00 / $15.00 | OpenAI 69% cheaper |
Legacy Models
| OpenAI Model | Price (in/out per 1M) | Best Bedrock Match | Bedrock Price | Winner |
|---|---|---|---|---|
| GPT-4 Turbo | $10.00 / $30.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | Bedrock 58% cheaper |
| GPT-4 | $30.00 / $60.00 | Claude Sonnet 4.6 | $3.00 / $15.00 | Bedrock 82% cheaper |
| GPT-3.5 Turbo | $0.50 / $1.50 | Llama 4 Maverick | $0.24 / $0.97 | Bedrock 42% cheaper + much better quality |
OpenAI Models on Bedrock (gpt-oss)
OpenAI's open-source models are available directly on Bedrock, enabling migration without switching model families:
| OpenAI Model | Price (in/out per 1M) | Bedrock gpt-oss | Bedrock Price | Notes |
|---|---|---|---|---|
| GPT-4o Mini | $0.15 / $0.60 | gpt-oss-120b | $0.15 / $0.60 | Same cost, runs on AWS infrastructure |
| GPT-5 Nano | $0.05 / $0.40 | gpt-oss-20b | $0.07 / $0.30 | Similar budget tier on AWS |
This path avoids model-family risk: the application stays on OpenAI-architecture models while consolidating on AWS infrastructure.
Percentages are blended savings using a 2:1 input-to-output token ratio. Actual savings depend on your input/output ratio.
Migration Decision Framework
Migrate to Bedrock if:
Using GPT-5.5 flagship → Bedrock 17% cheaper on output via Opus 4.6 ($5/$25 vs $5/$30); Sonnet 4.6 is 53% cheaper
Using Pro/expensive models (GPT-5.5 Pro, GPT-5.4 Pro, o1-pro) → 87-98% savings via Nova 2 Pro
Using Mini/Nano models at high volume → 87-94% savings via Nova Lite/Micro
Using legacy GPT-4/3.5 → 42-82% savings
Need AWS infrastructure integration
Need prompt caching (Claude only, 90% savings on cached content)
Using o3 for reasoning → DeepSeek-R1 on Bedrock is 32% cheaper
Want to stay on OpenAI models → gpt-oss on Bedrock (same models, AWS infrastructure)
Consider staying on OpenAI if:
Using GPT-5.5 for omnimodal (audio/video) → Claude is text+image only; GPT-5.5 has native audio/video
Using GPT-5.4 flagship → only 5% cheaper than Sonnet 4.6; marginal either way
Using mid-tier flagships (GPT-5, GPT-4.1, o3, o4-mini) → OpenAI 29-69% cheaper
Low volume (<$500/mo) where absolute savings are small
Heavily integrated with OpenAI ecosystem (Assistants API, gpt-image, Whisper, Realtime)
Need Realtime API (no Bedrock equivalent)
Analyze carefully: Calculate actual token usage x model-specific pricing. Small % differences matter at scale.
Feature Migration
| OpenAI Feature | Bedrock Equivalent | Notes |
|---|---|---|
| OpenAI SDK (direct) | Mantle OpenAI-compat endpoints | Zero code changes — set OPENAI_BASE_URL + API key + model ID |
| Function calling | Claude tools (excellent, similar format) | Minimal changes (works via Mantle or Converse API) |
| Streaming | All major models | Verify gateway format |
| Vision (GPT-4V) | Claude Sonnet/Haiku, Llama 4 Maverick | 70-95% cheaper |
| Embeddings (ada-002) | Titan Embeddings ($0.02/1M, 1536 dims) | Must re-embed all docs |
| DALL-E / gpt-image | Nova Canvas ($0.04-$0.08/img) | DALL-E EOL May 12, 2026; OpenAI replacement is gpt-image-1.5; Titan Image Gen v2 is Legacy (EOL Jun 30, 2026); use Nova Canvas |
| Whisper (STT) | Amazon Transcribe ($0.024/min) | 4x more expensive but more features |
| TTS | Amazon Polly | Different pricing model |
| Assistants API | See Assistants API decision tree below | Path depends on which Assistants features are used — see decision tree |
| JSON mode | Claude (excellent), Nova Pro (good) | Most models via prompt |
| Realtime API | No equivalent | Stay on OpenAI for this |
Common Migration Paths
OpenAI SDK → Mantle (minimal code changes)
If the application uses the OpenAI Python/JS SDK directly (from openai import OpenAI / new OpenAI()), Bedrock's Mantle OpenAI-compatible endpoints allow migration with minimal code changes — primarily environment variables plus a model string swap:
Set
OPENAI_BASE_URL=https://bedrock-mantle.{region}.api.aws/v1Set
OPENAI_API_KEY=<bedrock-api-key>— use a Bedrock API key, not your existing OpenAI API keyChange model string (e.g.,
gpt-5.4→anthropic.claude-sonnet-4-6oropenai.gpt-oss-120b)
Hard gates before recommending Mantle:
Model compatibility: Verify the selected Bedrock model supports the Responses API — check API compatibility. Not all models do. Do not recommend Mantle Responses API unless the target model is confirmed compatible.
Region availability: Mantle is available in 13 regions (us-east-1, us-east-2, us-west-2, ap-northeast-1, ap-south-1, ap-southeast-2, ap-southeast-3, eu-central-1, eu-west-1, eu-west-2, eu-south-1, eu-north-1, sa-east-1). If the target region is outside this list, do not recommend Mantle — use the boto3 Converse API path instead.
Supports Chat Completions API, Responses API, streaming, and stateful conversations.
Responses API capabilities (when stateful conversations matter):
Stateful conversation management — Bedrock rebuilds context automatically; no need to pass full conversation history on each request
Async / long-running inference — background processing for workloads that exceed typical request timeouts (useful for complex agentic tasks)
Streaming + non-streaming — both modes supported via the same endpoint
Assistants API → Migration Decision Tree
Assistants API and Responses API are different surfaces. Do not treat all Assistants API usage as an env-var-only migration. Apply this decision tree:
1. App already uses OpenAI Responses API (responses.create)
→ Mantle is the cleanest path. Env var swap + model string change. Minimal code changes.
2. App uses Assistants API only for stateful multi-turn conversation (no hosted tools, no file search, no code interpreter, no persistent Assistant objects, no complex run lifecycle)
→ Mantle Responses API is viable. Requires migrating from threads/runs calls to responses.create — this is a small API migration (days), not a full redesign. Not a zero-code-change swap.
3. App uses Assistants API with simple hosted tools (function calling only, no file search or code interpreter) → Mantle Responses API with tool use is viable. Moderate code migration (1-2 weeks) to adapt tool definitions and run lifecycle.
4. App uses Assistants API with file search, vector stores, code interpreter, persistent Assistant objects, or complex run lifecycle management → Do not recommend Mantle. Evaluate: Bedrock Agents (sessions, action groups, knowledge bases) for full agentic replacement (2-4 week migration), or app-managed orchestration if the team prefers to own state.
When to prefer Converse API over Mantle: If you need Bedrock-specific features (Guardrails, Knowledge Bases, prompt caching, Bedrock Agents integration) or your target region doesn't have Mantle. Mantle is the fastest path; Converse API is the most feature-complete path.
GPT-5.4 → Claude Sonnet 4.6
Near price parity (~5% difference). Migration case is driven by AWS consolidation, agentic reliability, or prompt caching — not cost. Both have ~200K+ context. Low risk.
GPT-5.4 Mini/Nano → Nova Lite/Micro
87-94% savings. Strong cost case at any volume. Nova Lite (300K context) covers most GPT-5.4 Mini use cases.
GPT-4/4 Turbo → Claude Sonnet 4.6
70-90% savings, similar or better quality, longer context (200K vs 128K). Low risk.
GPT-3.5 Turbo → Llama 4 Maverick
Similar cost, dramatically better quality, 1M context (vs 16K).
GPT-4 → Multi-Model (high spend)
Tier by complexity: simple → Nova Micro/Llama 4 Scout (60%), moderate → Llama 4 Maverick/Nova Pro (30%), complex → Claude Sonnet (10%). 85-95% savings.
Pro models → Nova 2 Pro
83-98% savings. Strong migration case at any volume. (Nova Premier v1 is Legacy — use Nova 2 Pro instead.)
Volume-Based Recommendations
Low (<1M tokens/day): Use best model for quality. Cost difference minimal.
Medium (1-10M tokens/day): Present cost comparison at volume. At 5M input + 2.5M output/day, evaluate per-model economics carefully.
High (10-100M tokens/day): Multi-model tiered approach recommended. Route by task complexity.
Very high (>100M tokens/day): Mandatory tiering:
Simple tasks (60%) → Nova Micro or Llama 4 Scout
Moderate tasks (30%) → Llama 4 Maverick or Nova Pro
Complex tasks (10%) → Claude Sonnet 4.6
OpenAI Pricing Tiers
OpenAI offers 4 tiers: Batch (50% off, 24hr), Flex (30-50% off, higher latency), Standard (baseline), Priority (2x, lowest latency). This guide uses Standard tier for comparison.
Requirements and compatibility
Written for: Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Names Claude Code tools, Needs an MCP server
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- cline/skills · skills/gcp-to-aws · commit 2637846
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